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Tumor Characteristics of Mohs Surgery Patients in Ottawa, Canada Versus Houston, Texas—A Consequence of Access to Care?

2011· article· en· W2056885885 on OpenAlexaffabout
Renée A. Beach, Tinghua Zhang, Leonard H. Goldberg, James D. Walker, Adam J. Mamelak

Bibliographic record

VenueDermatologic Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineMohs surgeryDemographicsIncidence (geometry)SurgerySkin cancerStage (stratigraphy)General surgeryCancerInternal medicineDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Ontario is one of the most underserved provinces in Canada for providing Mohs micrographic surgery (MMS). A new MMS clinic was opened in Ottawa, Ontario, in June 2009 to help combat the increasing incidence of nonmelanoma skin cancer (NMSC) in this region. OBJECTIVE: To prospectively compare MMS cases completed in Ottawa with cases completed in Houston, Texas, and examine the differences in tumor characteristics. MATERIALS AND METHODS: The first 150 cases performed in Ottawa were prospectively compared with 150 consecutive cases performed at a Mohs surgery clinic in Houston, Texas. Patient demographics, tumor diagnosis, primary or recurrent disease, tumor dimension, number of surgical stages, defect size, complexity of the procedure, and closure method were compared. RESULTS: The average preoperative tumor area was three times as great in Ottawa as in Houston. Almost one entire additional stage was required to clear the tumors treated in Ottawa. Postoperative defects were 87% larger in Ottawa. The number of advanced reconstructive repairs was significantly higher in Ottawa (93%) than Houston (14%). CONCLUSIONS: A significantly higher NMSC disease burden and greater surgical complexity was observed in the tumors treated in Ottawa than in Houston.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.267
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2011
Admission routes2
Has abstractyes

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